Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus.

ABSTRACT:

Introduction

Gestational diabetes mellitus (GDM) significantly affects pregnancy outcomes. Therefore, it is crucial to develop prediction models since they can guide timely interventions to reduce the incidence of GDM and its associated adverse effects.

Methods

A total of 554 pregnant women were selected and their sociodemographic characteristics, clinical data and dietary data were collected. Dietary data were investigated by a validated semi-quantitative food frequency questionnaire (FFQ). We applied random forest mean decrease impurity for feature selection and the models are built using logistic regression, XGBoost, and LightGBM algorithms. The prediction performance of different models was compared by accuracy, sensitivity, specificity, area under curve (AUC) and Hosmer

SUBMITTER: Ding T 

PROVIDER: S-EPMC11623027 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature